code: def ssd_512_resnet101_v2_voc(
      pretrained=False, pretrained_base=True, **kwargs
  ):
      """SSD architecture with ResNet v2 101 layers.
  
      Parameters
      ----------
      pretrained : bool or str
          Boolean value controls whether to load the default pretrained weights for model.
          String value represents the hashtag for a certain version of pretrained weights.
      pretrained_base : bool or str, optional, default is True
          Load pretrained base network, the extra layers are randomized.
      norm_layer : object
          Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
          Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
      norm_kwargs : dict
          Additional `norm_layer` arguments, for example `num_devices=4`
          for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
  
      Returns
      -------
      HybridBlock
          A SSD detection network.
      """
      classes = VOCDetection.CLASSES
      return get_ssd(
          "resnet101_v2",
          512,
          features=["stage3_activation22", "stage4_activation2"],
          filters=[512, 512, 256, 256],
          sizes=[51.2, 102.4, 189.4, 276.4, 363.52, 450.6, 492],
          ratios=[[1, 2, 0.5]]
          + [[1, 2, 0.5, 3, 1.0 / 3]] * 3
          + [[1, 2, 0.5]] * 2,
          steps=[16, 32, 64, 128, 256, 512],
          classes=classes,
          dataset="voc",
          pretrained=pretrained,
          pretrained_base=pretrained_base,
          **kwargs,
      )
  
imports: from __future__ import absolute_import
  import warnings
  from .ssd import get_ssd
  from .anchor import LiteAnchorGenerator
  from .vgg_atrous import vgg16_atrous_300, vgg16_atrous_512
  from ...data import VOCDetection
label_desc: Use image node_id 13 for calling a global function with example usage: ssd_512_resnet101_v2_voc(pretrained, pretrained_base) and returns: get_ssd